Dear Sir,

Greetings!!

We are in the process of editing a forthcoming book publication entitled **** Handbook of Machine Learning and IoT Applications for Health Informatics ****, to be published by CRC Press. We would like to take this opportunity to cordially invite you to submit a quality chapter proposal for consideration in this book.

We are familiar with your research interests and expertise(theory and practice) in the area of Machine Learning and IoT Applications for Health Informatics   and we are certain that your contribution on this topic, would make an excellent addition to this book.

The last date of full chapter submission is 1st Nov, 2023. Kindly try to submit one novel modern technique as a chapter  preferably in MS Word(up to  10,000 words). 

Feel free to contact us, we remain at your disposal,

Best Regards,

Editors,

You can send chapters to any one of the following email ids.

        sanjibanroy09@gmail.com

                           or 

        pijushsamui@gmail.com 

                       or

      zhangwg@cqu.edu.cn 

         or

        tag@granular.com 

This book will focus on the audiences comprising engineers, practising professionals in the industries, researchers, graduates and post-graduate students in academia, who are mainly working on IoT technology & its application in the medical field. This book shall help the research scholar working on machine learning and IoT for healthcare applications, computer scientists, bioinformatics analysts, IT professionals, PhD students, IoT industry professionals, software developers, nurses, and doctors.

This edited book will address the problems mentioned above and shall provide solutions. Each chapter shall address a unique machine and IoT-enabled application for health-related problems. The key features of this edited book are :

1. Application related to the amalgamations of machine learning and IoT for medical data

2. Explores the disease diagnosis incorporation powered by IoT and enabled with predictive models

3. Recent advancements in machine learning and deep learning models in health analytics.

4. Techniques that relate to the reduction of cost, treatment improvement, quick disease diagnosis, and drug and equipment management of healthcare using IoT systems.

5. Presents several case studies related to machine learning, deep learning, and IoT applications toward health analytics.